<i>Candida albicans</i> exhibits heterogeneous and adaptive cytoprotective responses to anti-fungal compounds
Bibliographic record
Abstract
Abstract Candida albicans is an opportunistic human pathogen which represents a significant threat to human health and is associated with substantial socio-economic burden. Current antifungal treatments fail at least in part because C. albicans can initiate a strong drug tolerance response, allowing cells to grow at concentrations above their minimal inhibitory concentration. Our goal is to better characterize this cytoprotective tolerance program at the molecular single cell level. We present here a nano-liter droplet-based fungal single cell transcriptomics platform capable of profiling thousands of individual C. albicans SC5314 cells in an efficient manner. Profiles of untreated cells partition into three transcriptional clusters with each highlighting a cell cycle checkpoint coupled with specific metabolic and stress responses, as perhaps expected. After just two days post-treatment with fluconazole, surviving cells bifurcate into two distinct subpopulations: the so-called α response involving upregulation of protein translation, rRNA processing and mitochondrial cellular respiration, and the β response involving processes and stress responses that assist damaged cells. By extending our time series to six days and profiling with other antifungals and bioactive compounds, we provide evidence that surviving cells transition from the α to β responses mediated by the Ribosome Assembly Stress Response (RASTR).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".